相关论文: Machine-Learning-based Prediction of Lattice Therm…
The lattice thermal conductivity of thermoelectric material CuInTe2 is predicted using classical molecular dynamics simulations, where a simple but effective Morse-type interatomic potential is constructed by fitting first-principles total…
We introduce machine learning (ML) models that predict the electronic structure of materials across a wide temperature range. Our models employ neural networks and are trained on density functional theory (DFT) data. Unlike most other ML…
The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been scarcely investigated despite its relevance to thermal…
Reliable artificial-intelligence models have the potential to accelerate the discovery of materials with optimal properties for various applications, including superconductivity, catalysis, and thermoelectricity. Advancements in this field…
Computational screening in heterogeneous catalysis relies increasingly on machine learning models for predicting key input parameters due to the high cost of computing these directly using first-principles methods. This becomes especially…
Compounds of low lattice thermal conductivity (LTC) are essential for seeking thermoelectric materials with high conversion efficiency. Some strategies have been used to decrease LTC. However, such trials have yielded successes only within…
We derive expressions for energy flow in terms of lattice normal mode coordinates and energy transmission involving reduced group velocities. With a version of Landauer formula appropriate for lattice dynamic approach, the phonon…
Accurately predicting lattice thermal conductivity (kL) from first principles remains a challenge in identifying materials with extreme thermal behavior. While modern lattice dynamics methods enable routine predictions of kL within the…
Many complex crystals show a flattening or even increasing lattice thermal conductivity at high temperatures, which deviates from the traditional 1/T decay trend given by conventional phonon theory. In this work, we predict the thermal…
The presence of lone pair (LP) electrons is strongly associated with the disruption of lattice heat transport, which is a critical component of strategies to achieve efficient thermoelectric energy conversion. By exploiting an empirical…
Micro/nano porous polymeric material is considered a unique industrial material due to its extremely low thermal conductivity, low density, and high surface area. Therefore, it is necessary to establish an accurate thermal conductivity…
Machine learning (ML) is increasingly becoming a helpful tool in the search for novel functional compounds. Here we use classification via random forests to predict the stability of half-Heusler (HH) compounds, using only experimentally…
We introduce a machine-learning density-functional-theory formalism for the spinless Hubbard model in one dimension at both zero and finite temperature. In the zero-temperature case this establishes a one-to-one relation between the site…
Heat management is crucial for state-of-the-art applications such as passive radiative cooling, thermally adjustable wearables, and camouflage systems. Their adaptive versions, to cater to varied requirements, lean on the potential of…
In recent years, nanostructuring of dielectric and semiconducting crystals has enhanced controllability of their thermal conductivity. To carry out computational material search for nanostructured materials with desirable thermal…
Transition metal dichalcogenides are investigated for various applications at the nanoscale thanks to their unique combination of properties and dimensionality. For many of the anticipated applications, heat conduction plays an important…
The electronic and magnetic properties of Ti\raisebox{-.2ex}{\scriptsize 2}CoSi Heusler compound are investigated using density functional calculations. The optimized lattice constant is found to be 6.030 A. The compound is a half-metallic…
Predicting the critical temperature $T_c$ of new superconductors is a notoriously difficult task, even for electron-phonon paired superconductors for which the theory is relatively well understood. Early attempts by McMillan and Allen and…
Half-Heusler (HH) phases have garnered much attention as thermally stable and non-toxic thermoelectric materials for power conversion. The most studied alloys to date utilize Hf, Zr, and Ti as the base components. These alloys can achieve a…
The search for materials with topological properties is an ongoing effort. In this article we propose a systematic statistical method supported by machine learning techniques that is capable of constructing topological models for a generic…